Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning

Tania Akter; Mohammad Hanif Ali; Md Shahriare Satu; Md Imran Khan; Mufti Mahmud

doi:10.1007/978-3-030-86993-9_36

Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning

Tania Akter, Mohammad Hanif Ali, Md Shahriare Satu, Md Imran Khan, Mufti Mahmud^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

31 Scopus citations

Abstract

Autism spectrum disorder (ASD) is a neuro-developmental disease that has a lifetime impact on a person’s ability to interact and communicate with others. Early discovery of autism can assist to prepare a plan for suitable therapy and reduce its impact on patients at an appropriate time. The aim of this work is to propose a machine learning model which generates autism subtypes and identifies discriminatory factors among them. In this work, we use Quantitative Checklist for Autism in Toddlers-10 (Q-CHAT-10) of toddler and Autism Spectrum Quotient-10 (AQ-10) datasets of child, adolescent, and adult screening datasets respectively. Then, only autism records are merged and implemented k-means algorithm to extract various autism subtypes. According to Silhoutte score, we select the best autism dataset and balance its subtypes using random oversampling (ROS) and synthetic minority oversampling technique for numeric and categorical values (SMOTENC). Afterwards, various classifiers are employed into both primary dataset and its balanced subtypes. In this work, logistic regression shows the highest result for primary dataset. Also, it achieves the greatest results for ROS and SMOTENC datasets. Hence, shapely adaptive explanation (SHAP) technique is used to rank features and scrutinized discriminatory factors of these autism subtypes.

Original language	English
Title of host publication	Brain Informatics - 14th International Conference, BI 2021, Proceedings
Editors	Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	401-410
Number of pages	10
ISBN (Print)	9783030869922
DOIs	https://doi.org/10.1007/978-3-030-86993-9_36
State	Published - 2021
Externally published	Yes
Event	14th International Conference on Brain Informatics, BI 2021 - Virtual, Online Duration: 17 Sep 2021 → 19 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12960 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Brain Informatics, BI 2021
City	Virtual, Online
Period	17/09/21 → 19/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Autism
Discriminatory factors
K-means clustering
Machine learning
SHAP analysis

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-86993-9_36

Cite this

Akter, T., Ali, M. H., Satu, M. S., Khan, M. I., & Mahmud, M. (2021). Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning. In M. Mahmud, M. S. Kaiser, S. Vassanelli, Q. Dai, & N. Zhong (Eds.), Brain Informatics - 14th International Conference, BI 2021, Proceedings (pp. 401-410). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86993-9_36

Akter, Tania ; Ali, Mohammad Hanif ; Satu, Md Shahriare et al. / Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning. Brain Informatics - 14th International Conference, BI 2021, Proceedings. editor / Mufti Mahmud ; M Shamim Kaiser ; Stefano Vassanelli ; Qionghai Dai ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2021. pp. 401-410 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Autism spectrum disorder (ASD) is a neuro-developmental disease that has a lifetime impact on a person{\textquoteright}s ability to interact and communicate with others. Early discovery of autism can assist to prepare a plan for suitable therapy and reduce its impact on patients at an appropriate time. The aim of this work is to propose a machine learning model which generates autism subtypes and identifies discriminatory factors among them. In this work, we use Quantitative Checklist for Autism in Toddlers-10 (Q-CHAT-10) of toddler and Autism Spectrum Quotient-10 (AQ-10) datasets of child, adolescent, and adult screening datasets respectively. Then, only autism records are merged and implemented k-means algorithm to extract various autism subtypes. According to Silhoutte score, we select the best autism dataset and balance its subtypes using random oversampling (ROS) and synthetic minority oversampling technique for numeric and categorical values (SMOTENC). Afterwards, various classifiers are employed into both primary dataset and its balanced subtypes. In this work, logistic regression shows the highest result for primary dataset. Also, it achieves the greatest results for ROS and SMOTENC datasets. Hence, shapely adaptive explanation (SHAP) technique is used to rank features and scrutinized discriminatory factors of these autism subtypes.",

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Akter, T, Ali, MH, Satu, MS, Khan, MI & Mahmud, M 2021, Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning. in M Mahmud, MS Kaiser, S Vassanelli, Q Dai & N Zhong (eds), Brain Informatics - 14th International Conference, BI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12960 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 401-410, 14th International Conference on Brain Informatics, BI 2021, Virtual, Online, 17/09/21. https://doi.org/10.1007/978-3-030-86993-9_36

Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning. / Akter, Tania; Ali, Mohammad Hanif; Satu, Md Shahriare et al.
Brain Informatics - 14th International Conference, BI 2021, Proceedings. ed. / Mufti Mahmud; M Shamim Kaiser; Stefano Vassanelli; Qionghai Dai; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2021. p. 401-410 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12960 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning

AU - Akter, Tania

AU - Ali, Mohammad Hanif

AU - Satu, Md Shahriare

AU - Khan, Md Imran

AU - Mahmud, Mufti

PY - 2021

Y1 - 2021

N2 - Autism spectrum disorder (ASD) is a neuro-developmental disease that has a lifetime impact on a person’s ability to interact and communicate with others. Early discovery of autism can assist to prepare a plan for suitable therapy and reduce its impact on patients at an appropriate time. The aim of this work is to propose a machine learning model which generates autism subtypes and identifies discriminatory factors among them. In this work, we use Quantitative Checklist for Autism in Toddlers-10 (Q-CHAT-10) of toddler and Autism Spectrum Quotient-10 (AQ-10) datasets of child, adolescent, and adult screening datasets respectively. Then, only autism records are merged and implemented k-means algorithm to extract various autism subtypes. According to Silhoutte score, we select the best autism dataset and balance its subtypes using random oversampling (ROS) and synthetic minority oversampling technique for numeric and categorical values (SMOTENC). Afterwards, various classifiers are employed into both primary dataset and its balanced subtypes. In this work, logistic regression shows the highest result for primary dataset. Also, it achieves the greatest results for ROS and SMOTENC datasets. Hence, shapely adaptive explanation (SHAP) technique is used to rank features and scrutinized discriminatory factors of these autism subtypes.

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KW - Discriminatory factors

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A2 - Kaiser, M Shamim

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Akter T, Ali MH, Satu MS, Khan MI, Mahmud M. Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning. In Mahmud M, Kaiser MS, Vassanelli S, Dai Q, Zhong N, editors, Brain Informatics - 14th International Conference, BI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 401-410. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-86993-9_36

Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this